HackerNoon Article Warns of "Observability Debt" Masking System Failures by 2025

A recent article published on HackerNoon, authored by Software Engineer Jeet Mehta and Rakshith Vijayakumar Aralimatti, highlights a critical concern in software development: the rise of "observability debt" and its potential to conceal systemic issues despite seemingly perfect dashboards. The piece, titled "The Observability Debt Hypothesis: Why Perfect Dashboards Still Mask Failing Systems," was published on November 3, 2025, and emphasizes that relying solely on surface-level metrics can distort truth and weaken engineering judgment.

"Perfect dashboards don’t mean perfect systems. Explore how observability debt hides behind metrics, distorts truth, and weakens engineering judgment in 2025," stated HackerNoon in a promotional tweet.

Observability, in the context of IT and cloud computing, refers to the ability to infer the internal state of a system by examining its external outputs, typically logs, metrics, and traces. It is crucial for understanding complex, distributed systems and proactively identifying and resolving issues. However, observability debt arises when organizations fail to adequately instrument their systems, leading to gaps in monitoring and an incomplete understanding of system health.

This debt can manifest as misleading metrics that paint an overly optimistic picture, while underlying problems like inefficient code, poor architecture, or unaddressed "unknown unknowns" persist unnoticed. Experts suggest that as systems become more intricate with microservices and cloud-native architectures, the risk of accumulating this debt increases significantly. The article likely delves into how this can lead to delayed incident response, increased operational costs, and ultimately, a compromised user experience.

The HackerNoon discussion underscores the importance of a holistic approach to observability, moving beyond basic monitoring to gain deep, actionable insights. It advocates for comprehensive instrumentation and analysis to prevent critical issues from being obscured by superficial data, urging engineers to critically evaluate their monitoring strategies to ensure true system reliability and performance in the evolving technological landscape.